Visibility of outdoor images is often degraded by turbid medium in poor weather such as haze, fog, sandstorms, and so on. Optically, poor visibility in digital images is due to the substantial presence of different atmospheric particles which absorb and scatter light between a digital camera and a captured object. Image degradation may cause problems for many systems which must operate under a wide range of weather conditions such as outdoor object recognition systems, obstacle detection systems, video surveillance systems, and intelligent transportation systems.
To facilitate the restoration of visibility and the recovery of vivid colors in degraded images, the technique of haze removal has recently emerged as an effective image-restoration tool. It utilizes scene depth information to produce haze-free images for a wide range of computational vision applications. Consequentially, numerous haze removal methods have been proposed.
According to previous research, the conventional haze removal methods may fit into two major categories: single image information and non-single image information.
Non-single image information approaches remove haze by utilizing either multiple images or additional geometrical information for a same scene via special hardware in order to estimate the unknown depth information, thereby removing haze formation and restoring visibility. However, the use of non-single image information approaches often requires additional hardware. This results in added expense and increased technique complexity.
Recently, research conducted in the area of image haze removal has been oriented towards single-image information approaches, by which a single image is used to achieve complete restoration. This is accomplished by employing stronger priors or assumptions when determining the difference between a haze-free image and an incoming hazy image. A prior art method proposes a single-image restoration technique that removes haze by maximizing the local contrast of recovered scene radiance based on an observation that captured hazy images have lower contrast than restored images. However, such technique may result in unwanted feature artifact effects along depth edges. Another prior art method proposes another single-image restoration technique that estimates the albedo of the scene and deduces the transmission map based on an assumption that the transmission shading and the surface shading are locally uncorrelated. However, such technique may not contend with images featuring dense fog.
Yet another prior art proposes a dark channel prior method which uses a key assumption that most local patches for outdoor haze-free images exhibit very low intensity in at least one of color channel, which can be used to directly estimate haze density and recover vivid colors. Until now, such approach has attracted the most attention due to its ability to effectively remove haze formation while only using a single image. Inspired by the dark channel prior technique, an improved haze removal algorithm is proposed by employing a scheme consisting of a dark channel prior and a multi-scale Retinex technique for quickly restoring hazy images. However, the presence of localized light sources and color shifts commonly encountered during inclement weather conditions further complicate the process. As such, the dark channel prior-based methods cannot produce satisfactory restoration results under these circumstances.